https://github.com/liu-yushan/PoLo
공식 코드Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
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A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs
TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphs
Context-Aware Adapter Tuning for Few-Shot Relation Learning in Knowledge Graphs
Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs
Hi-ArG: Exploring the Integration of Hierarchical Argumentation Graphs in Language Pretraining
Using Knowledge Graphs to harvest datasets for efficient CLIP model training
RAKG:Document-level Retrieval Augmented Knowledge Graph Construction
Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering
Representing Syntax and Composition with Geometric Transformations
NePTuNe: Neural Powered Tucker Network for Knowledge Graph Completion
Zero-Shot Relational Learning for Multimodal Knowledge Graphs
Multimodal Learning on Graphs for Disease Relation Extraction
VRKG4Rec: Virtual Relational Knowledge Graphs for Recommendation
An Accurate Unsupervised Method for Joint Entity Alignment and Dangling Entity Detection
ForPKG: A Framework for Constructing Forestry Policy Knowledge Graph and Application Analysis
TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge
ASGEA: Exploiting Logic Rules from Align-Subgraphs for Entity Alignment
Multi-modal Contrastive Representation Learning for Entity Alignment
Knowledge Graph-based Retrieval-Augmented Generation for Schema Matching
Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunities
Improving Inductive Link Prediction Using Hyper-Relational Facts
Neural Graph Embedding Methods for Natural Language Processing
InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactions
CESI: Canonicalizing Open Knowledge Bases using Embeddings and Side Information
Knowledge-augmented Pre-trained Language Models for Biomedical Relation Extraction
DARWIN Series: Domain Specific Large Language Models for Natural Science
DARWIN Series: Domain Specific Large Language Models for Natural Science
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.